Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations16743
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory121.0 B

Variable types

Numeric8
DateTime1
Categorical1
Text4

Alerts

avg_temp is highly overall correlated with max_temp and 1 other fieldsHigh correlation
max_temp is highly overall correlated with avg_temp and 1 other fieldsHigh correlation
min_temp is highly overall correlated with avg_temp and 1 other fieldsHigh correlation
year is highly imbalanced (86.5%) Imbalance
precipitation has 4324 (25.8%) zeros Zeros
wind_direction has 223 (1.3%) zeros Zeros
wind_speed has 223 (1.3%) zeros Zeros

Reproduction

Analysis started2025-08-16 14:36:01.942292
Analysis finished2025-08-16 14:36:11.341698
Duration9.4 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

precipitation
Real number (ℝ)

Zeros 

Distinct565
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57909037
Minimum0
Maximum20.89
Zeros4324
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2025-08-16T10:36:11.492306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.19
Q30.75
95-th percentile2.38
Maximum20.89
Range20.89
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.98805716
Coefficient of variation (CV)1.7062228
Kurtosis34.213837
Mean0.57909037
Median Absolute Deviation (MAD)0.19
Skewness4.2079949
Sum9695.71
Variance0.97625695
MonotonicityNot monotonic
2025-08-16T10:36:11.642712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4324
25.8%
0.01 549
 
3.3%
0.02 366
 
2.2%
0.03 333
 
2.0%
0.04 279
 
1.7%
0.05 249
 
1.5%
0.06 205
 
1.2%
0.11 199
 
1.2%
0.07 195
 
1.2%
0.08 194
 
1.2%
Other values (555) 9850
58.8%
ValueCountFrequency (%)
0.0 4324
25.8%
0.01 549
 
3.3%
0.02 366
 
2.2%
0.03 333
 
2.0%
0.04 279
 
1.7%
0.05 249
 
1.5%
0.06 205
 
1.2%
0.07 195
 
1.2%
0.08 194
 
1.2%
0.09 193
 
1.2%
ValueCountFrequency (%)
20.89 1
< 0.1%
15.19 1
< 0.1%
14.03 1
< 0.1%
13.36 1
< 0.1%
12.65 1
< 0.1%
12.02 1
< 0.1%
11.52 1
< 0.1%
10.58 1
< 0.1%
10.49 1
< 0.1%
10.34 1
< 0.1%

avg_temp
Real number (ℝ)

High correlation 

Distinct119
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.089112
Minimum-27
Maximum100
Zeros15
Zeros (%)0.1%
Negative79
Negative (%)0.5%
Memory size147.3 KiB
2025-08-16T10:36:11.785155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-27
5-th percentile23
Q144
median58
Q371
95-th percentile83
Maximum100
Range127
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.798295
Coefficient of variation (CV)0.33515051
Kurtosis-0.013455145
Mean56.089112
Median Absolute Deviation (MAD)14
Skewness-0.55250384
Sum939100
Variance353.37588
MonotonicityNot monotonic
2025-08-16T10:36:11.940650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 643
 
3.8%
58 338
 
2.0%
74 329
 
2.0%
57 328
 
2.0%
56 325
 
1.9%
72 322
 
1.9%
75 320
 
1.9%
55 319
 
1.9%
59 315
 
1.9%
61 314
 
1.9%
Other values (109) 13190
78.8%
ValueCountFrequency (%)
-27 1
 
< 0.1%
-21 2
< 0.1%
-20 1
 
< 0.1%
-19 2
< 0.1%
-18 3
< 0.1%
-15 1
 
< 0.1%
-14 1
 
< 0.1%
-13 3
< 0.1%
-11 2
< 0.1%
-10 1
 
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 1
 
< 0.1%
98 4
 
< 0.1%
97 2
 
< 0.1%
95 1
 
< 0.1%
94 3
 
< 0.1%
93 8
< 0.1%
92 7
< 0.1%
91 10
0.1%
90 13
0.1%

max_temp
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.042406
Minimum-19
Maximum111
Zeros6
Zeros (%)< 0.1%
Negative31
Negative (%)0.2%
Memory size147.3 KiB
2025-08-16T10:36:12.091822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile30
Q153
median68
Q382
95-th percentile93
Maximum111
Range130
Interquartile range (IQR)29

Descriptive statistics

Standard deviation19.787954
Coefficient of variation (CV)0.29962497
Kurtosis-0.056357295
Mean66.042406
Median Absolute Deviation (MAD)14
Skewness-0.5928578
Sum1105748
Variance391.56313
MonotonicityNot monotonic
2025-08-16T10:36:12.248813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 592
 
3.5%
82 390
 
2.3%
84 366
 
2.2%
83 363
 
2.2%
81 360
 
2.2%
86 359
 
2.1%
80 349
 
2.1%
85 335
 
2.0%
70 325
 
1.9%
66 311
 
1.9%
Other values (115) 12993
77.6%
ValueCountFrequency (%)
-19 1
 
< 0.1%
-16 1
 
< 0.1%
-15 1
 
< 0.1%
-11 4
< 0.1%
-10 2
< 0.1%
-9 1
 
< 0.1%
-8 1
 
< 0.1%
-7 2
< 0.1%
-6 1
 
< 0.1%
-5 3
< 0.1%
ValueCountFrequency (%)
111 2
 
< 0.1%
110 4
 
< 0.1%
109 4
 
< 0.1%
107 6
 
< 0.1%
106 4
 
< 0.1%
105 9
 
0.1%
104 13
0.1%
103 22
0.1%
102 13
0.1%
101 29
0.2%

min_temp
Real number (ℝ)

High correlation 

Distinct114
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.642716
Minimum-35
Maximum88
Zeros40
Zeros (%)0.2%
Negative214
Negative (%)1.3%
Memory size147.3 KiB
2025-08-16T10:36:12.396048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-35
5-th percentile14
Q133
median47
Q360
95-th percentile73
Maximum88
Range123
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.559263
Coefficient of variation (CV)0.40662047
Kurtosis-0.11371736
Mean45.642716
Median Absolute Deviation (MAD)13
Skewness-0.43089864
Sum764196
Variance344.44623
MonotonicityNot monotonic
2025-08-16T10:36:12.551455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 550
 
3.3%
52 343
 
2.0%
49 331
 
2.0%
47 328
 
2.0%
46 325
 
1.9%
51 324
 
1.9%
54 320
 
1.9%
53 318
 
1.9%
58 316
 
1.9%
45 314
 
1.9%
Other values (104) 13274
79.3%
ValueCountFrequency (%)
-35 1
 
< 0.1%
-28 2
< 0.1%
-27 2
< 0.1%
-26 3
< 0.1%
-24 1
 
< 0.1%
-23 1
 
< 0.1%
-21 3
< 0.1%
-20 3
< 0.1%
-19 3
< 0.1%
-18 2
< 0.1%
ValueCountFrequency (%)
88 2
 
< 0.1%
87 2
 
< 0.1%
85 4
 
< 0.1%
83 2
 
< 0.1%
82 10
 
0.1%
81 13
 
0.1%
80 32
 
0.2%
79 44
0.3%
78 74
0.4%
77 92
0.5%

wind_direction
Real number (ℝ)

Zeros 

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.791316
Minimum0
Maximum36
Zeros223
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2025-08-16T10:36:12.703146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q115
median19
Q323
95-th percentile28
Maximum36
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.4615273
Coefficient of variation (CV)0.3438571
Kurtosis0.21101361
Mean18.791316
Median Absolute Deviation (MAD)4
Skewness-0.48168313
Sum314623
Variance41.751335
MonotonicityNot monotonic
2025-08-16T10:36:12.954913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
21 1134
 
6.8%
20 1087
 
6.5%
22 1065
 
6.4%
19 1046
 
6.2%
18 991
 
5.9%
23 990
 
5.9%
17 916
 
5.5%
16 896
 
5.4%
24 862
 
5.1%
25 777
 
4.6%
Other values (27) 6979
41.7%
ValueCountFrequency (%)
0 223
1.3%
1 21
 
0.1%
2 48
 
0.3%
3 63
 
0.4%
4 126
0.8%
5 146
0.9%
6 135
0.8%
7 168
1.0%
8 228
1.4%
9 310
1.9%
ValueCountFrequency (%)
36 16
 
0.1%
35 20
 
0.1%
34 32
 
0.2%
33 42
 
0.3%
32 76
 
0.5%
31 126
 
0.8%
30 183
 
1.1%
29 263
1.6%
28 397
2.4%
27 519
3.1%

wind_speed
Real number (ℝ)

Zeros 

Distinct1461
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3298202
Minimum0
Maximum61.1
Zeros223
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2025-08-16T10:36:13.099168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.81
Q14.04
median5.94
Q38.08
95-th percentile11.95
Maximum61.1
Range61.1
Interquartile range (IQR)4.04

Descriptive statistics

Standard deviation3.4947853
Coefficient of variation (CV)0.55211446
Kurtosis20.740846
Mean6.3298202
Median Absolute Deviation (MAD)2
Skewness2.4603892
Sum105980.18
Variance12.213524
MonotonicityNot monotonic
2025-08-16T10:36:13.248687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 223
 
1.3%
5.7 48
 
0.3%
5.6 48
 
0.3%
7.2 46
 
0.3%
6.3 46
 
0.3%
5.9 46
 
0.3%
5.1 46
 
0.3%
5.4 45
 
0.3%
4.7 45
 
0.3%
4.6 44
 
0.3%
Other values (1451) 16106
96.2%
ValueCountFrequency (%)
0.0 223
1.3%
0.15 1
 
< 0.1%
0.18 2
 
< 0.1%
0.2 2
 
< 0.1%
0.25 2
 
< 0.1%
0.26 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 6
 
< 0.1%
0.32 1
 
< 0.1%
0.34 1
 
< 0.1%
ValueCountFrequency (%)
61.1 1
< 0.1%
52.6 1
< 0.1%
50.06 1
< 0.1%
49.6 1
< 0.1%
47.56 1
< 0.1%
47.2 1
< 0.1%
45.16 1
< 0.1%
43.85 1
< 0.1%
43.34 1
< 0.1%
43.25 1
< 0.1%
Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
Minimum2016-01-03 00:00:00
Maximum2017-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-16T10:36:13.391167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:13.534001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2016
16426 
2017
 
317

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters66972
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 16426
98.1%
2017 317
 
1.9%

Length

2025-08-16T10:36:13.671344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-16T10:36:13.787586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 16426
98.1%
2017 317
 
1.9%

Most occurring characters

ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 16743
25.0%
0 16743
25.0%
1 16743
25.0%
6 16426
24.5%
7 317
 
0.5%

month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3431285
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2025-08-16T10:36:13.893969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.490723
Coefficient of variation (CV)0.55031566
Kurtosis-1.2144816
Mean6.3431285
Median Absolute Deviation (MAD)3
Skewness0.0069942293
Sum106203
Variance12.185147
MonotonicityNot monotonic
2025-08-16T10:36:14.007353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1892
11.3%
5 1585
9.5%
10 1585
9.5%
7 1572
9.4%
8 1268
7.6%
12 1268
7.6%
4 1265
7.6%
9 1265
7.6%
3 1262
7.5%
6 1262
7.5%
Other values (2) 2519
15.0%
ValueCountFrequency (%)
1 1892
11.3%
2 1260
7.5%
3 1262
7.5%
4 1265
7.6%
5 1585
9.5%
6 1262
7.5%
7 1572
9.4%
8 1268
7.6%
9 1265
7.6%
10 1585
9.5%
ValueCountFrequency (%)
12 1268
7.6%
11 1259
7.5%
10 1585
9.5%
9 1265
7.6%
8 1268
7.6%
7 1572
9.4%
6 1262
7.5%
5 1585
9.5%
4 1265
7.6%
3 1262
7.5%

week_of
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.650242
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.3 KiB
2025-08-16T10:36:14.217450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9234249
Coefficient of variation (CV)0.57017808
Kurtosis-1.1987441
Mean15.650242
Median Absolute Deviation (MAD)8
Skewness0.024204995
Sum262032
Variance79.627513
MonotonicityNot monotonic
2025-08-16T10:36:14.357532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 948
 
5.7%
17 945
 
5.6%
24 945
 
5.6%
10 945
 
5.6%
6 634
 
3.8%
1 634
 
3.8%
4 634
 
3.8%
18 633
 
3.8%
25 633
 
3.8%
11 633
 
3.8%
Other values (21) 9159
54.7%
ValueCountFrequency (%)
1 634
3.8%
2 317
 
1.9%
3 948
5.7%
4 634
3.8%
5 317
 
1.9%
6 634
3.8%
7 632
3.8%
8 317
 
1.9%
9 317
 
1.9%
10 945
5.6%
ValueCountFrequency (%)
31 629
3.8%
30 317
 
1.9%
29 317
 
1.9%
28 632
3.8%
27 629
3.8%
26 315
 
1.9%
25 633
3.8%
24 945
5.6%
23 317
 
1.9%
22 317
 
1.9%

city
Text

Distinct307
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2025-08-16T10:36:14.642091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length8.8165203
Min length3

Characters and Unicode

Total characters147615
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBirmingham
2nd rowHuntsville
3rd rowMobile
4th rowMontgomery
5th rowAnchorage
ValueCountFrequency (%)
city 477
 
2.2%
san 265
 
1.2%
falls 265
 
1.2%
lake 253
 
1.2%
beach 212
 
1.0%
fort 212
 
1.0%
st 212
 
1.0%
grand 212
 
1.0%
island 159
 
0.7%
santa 106
 
0.5%
Other values (337) 18853
88.8%
2025-08-16T10:36:15.088535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13728
 
9.3%
e 11833
 
8.0%
o 10641
 
7.2%
n 10605
 
7.2%
l 9902
 
6.7%
i 9296
 
6.3%
r 8292
 
5.6%
t 8190
 
5.5%
s 6818
 
4.6%
u 4753
 
3.2%
Other values (45) 53557
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 147615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13728
 
9.3%
e 11833
 
8.0%
o 10641
 
7.2%
n 10605
 
7.2%
l 9902
 
6.7%
i 9296
 
6.3%
r 8292
 
5.6%
t 8190
 
5.5%
s 6818
 
4.6%
u 4753
 
3.2%
Other values (45) 53557
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 147615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13728
 
9.3%
e 11833
 
8.0%
o 10641
 
7.2%
n 10605
 
7.2%
l 9902
 
6.7%
i 9296
 
6.3%
r 8292
 
5.6%
t 8190
 
5.5%
s 6818
 
4.6%
u 4753
 
3.2%
Other values (45) 53557
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 147615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13728
 
9.3%
e 11833
 
8.0%
o 10641
 
7.2%
n 10605
 
7.2%
l 9902
 
6.7%
i 9296
 
6.3%
r 8292
 
5.6%
t 8190
 
5.5%
s 6818
 
4.6%
u 4753
 
3.2%
Other values (45) 53557
36.3%

code
Text

Distinct318
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2025-08-16T10:36:15.498023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters50229
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBHM
2nd rowHSV
3rd rowMOB
4th rowMGM
5th rowANC
ValueCountFrequency (%)
bhm 53
 
0.3%
gkn 53
 
0.3%
ann 53
 
0.3%
bet 53
 
0.3%
btt 53
 
0.3%
cdb 53
 
0.3%
inw 53
 
0.3%
cdv 53
 
0.3%
fai 53
 
0.3%
hom 53
 
0.3%
Other values (308) 16213
96.8%
2025-08-16T10:36:16.033908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.5%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.0%
D 2479
 
4.9%
I 2426
 
4.8%
Other values (19) 20695
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.5%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.0%
D 2479
 
4.9%
I 2426
 
4.8%
Other values (19) 20695
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.5%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.0%
D 2479
 
4.9%
I 2426
 
4.8%
Other values (19) 20695
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3865
 
7.7%
S 3804
 
7.6%
L 3286
 
6.5%
T 3061
 
6.1%
B 2756
 
5.5%
C 2756
 
5.5%
G 2566
 
5.1%
N 2535
 
5.0%
D 2479
 
4.9%
I 2426
 
4.8%
Other values (19) 20695
41.2%
Distinct318
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2025-08-16T10:36:16.290853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length12.81652
Min length7

Characters and Unicode

Total characters214587
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBirmingham, AL
2nd rowHuntsville, AL
3rd rowMobile, AL
4th rowMontgomery, AL
5th rowAnchorage, AK
ValueCountFrequency (%)
ak 1719
 
4.5%
tx 1272
 
3.4%
ca 999
 
2.6%
fl 636
 
1.7%
mt 630
 
1.7%
mi 477
 
1.3%
city 477
 
1.3%
or 424
 
1.1%
ne 424
 
1.1%
ny 424
 
1.1%
Other values (386) 30487
80.3%
2025-08-16T10:36:16.675707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21226
 
9.9%
, 16743
 
7.8%
a 13728
 
6.4%
e 11833
 
5.5%
o 10641
 
5.0%
n 10605
 
4.9%
l 9902
 
4.6%
i 9296
 
4.3%
r 8292
 
3.9%
t 8190
 
3.8%
Other values (48) 94131
43.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 214587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21226
 
9.9%
, 16743
 
7.8%
a 13728
 
6.4%
e 11833
 
5.5%
o 10641
 
5.0%
n 10605
 
4.9%
l 9902
 
4.6%
i 9296
 
4.3%
r 8292
 
3.9%
t 8190
 
3.8%
Other values (48) 94131
43.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 214587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21226
 
9.9%
, 16743
 
7.8%
a 13728
 
6.4%
e 11833
 
5.5%
o 10641
 
5.0%
n 10605
 
4.9%
l 9902
 
4.6%
i 9296
 
4.3%
r 8292
 
3.9%
t 8190
 
3.8%
Other values (48) 94131
43.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 214587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21226
 
9.9%
, 16743
 
7.8%
a 13728
 
6.4%
e 11833
 
5.5%
o 10641
 
5.0%
n 10605
 
4.9%
l 9902
 
4.6%
i 9296
 
4.3%
r 8292
 
3.9%
t 8190
 
3.8%
Other values (48) 94131
43.9%

state
Text

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size130.9 KiB
2025-08-16T10:36:16.896480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.932091
Min length2

Characters and Unicode

Total characters132807
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlabama
3rd rowAlabama
4th rowAlabama
5th rowAlaska
ValueCountFrequency (%)
alaska 1719
 
9.1%
texas 1272
 
6.7%
california 999
 
5.3%
new 789
 
4.2%
florida 636
 
3.4%
north 636
 
3.4%
carolina 583
 
3.1%
montana 583
 
3.1%
dakota 530
 
2.8%
virginia 530
 
2.8%
Other values (46) 10686
56.4%
2025-08-16T10:36:17.262876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20409
15.4%
i 13114
 
9.9%
n 10686
 
8.0%
s 10528
 
7.9%
o 10376
 
7.8%
e 7492
 
5.6%
r 6558
 
4.9%
l 5898
 
4.4%
t 4016
 
3.0%
k 3733
 
2.8%
Other values (37) 39997
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20409
15.4%
i 13114
 
9.9%
n 10686
 
8.0%
s 10528
 
7.9%
o 10376
 
7.8%
e 7492
 
5.6%
r 6558
 
4.9%
l 5898
 
4.4%
t 4016
 
3.0%
k 3733
 
2.8%
Other values (37) 39997
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20409
15.4%
i 13114
 
9.9%
n 10686
 
8.0%
s 10528
 
7.9%
o 10376
 
7.8%
e 7492
 
5.6%
r 6558
 
4.9%
l 5898
 
4.4%
t 4016
 
3.0%
k 3733
 
2.8%
Other values (37) 39997
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20409
15.4%
i 13114
 
9.9%
n 10686
 
8.0%
s 10528
 
7.9%
o 10376
 
7.8%
e 7492
 
5.6%
r 6558
 
4.9%
l 5898
 
4.4%
t 4016
 
3.0%
k 3733
 
2.8%
Other values (37) 39997
30.1%

Interactions

2025-08-16T10:36:09.541870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.044026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.932263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.891238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.931176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.830420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.752006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.656499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:09.646622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.152063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.046089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.015262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.039894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.940120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.862300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.766940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:09.856156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.259374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.162224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.127795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.149570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.056447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.980909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.878353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:09.972907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.365874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.292824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.240957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.258920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.169004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.093167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.987255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:10.081081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.478687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.410403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.357820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.374554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.287075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.201881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:09.096854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:10.197026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.587743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.537431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.474874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.488431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.404929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.313781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:09.213752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:10.304160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.698180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.654122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.676656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.605141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.518837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.425167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:09.324980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:10.415129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:03.809514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:04.768437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:05.796261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:06.717943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:07.635473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:08.538623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-16T10:36:09.431072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-08-16T10:36:17.387716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
avg_tempmax_tempmin_tempmonthprecipitationweek_ofwind_directionwind_speedyear
avg_temp1.0000.9770.9750.2380.0320.050-0.195-0.1810.131
max_temp0.9771.0000.9090.231-0.0270.062-0.151-0.2040.131
min_temp0.9750.9091.0000.2360.0940.037-0.233-0.1500.121
month0.2380.2310.2361.000-0.0180.022-0.086-0.1330.288
precipitation0.032-0.0270.094-0.0181.0000.123-0.1150.0370.000
week_of0.0500.0620.0370.0220.1231.000-0.031-0.0080.328
wind_direction-0.195-0.151-0.233-0.086-0.115-0.0311.0000.0600.070
wind_speed-0.181-0.204-0.150-0.1330.037-0.0080.0601.0000.056
year0.1310.1310.1210.2880.0000.3280.0700.0561.000

Missing values

2025-08-16T10:36:10.585515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-16T10:36:10.837445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

precipitationavg_tempmax_tempmin_tempwind_directionwind_speeddate_fullyearmonthweek_ofcitycodelocationstate
00.0394632334.332016-01-03201613BirminghamBHMBirmingham, ALAlabama
10.0394731323.862016-01-03201613HuntsvilleHSVHuntsville, ALAlabama
20.16465141359.732016-01-03201613MobileMOBMobile, ALAlabama
30.0455238326.862016-01-03201613MontgomeryMGMMontgomery, ALAlabama
40.01343829197.82016-01-03201613AnchorageANCAnchorage, AKAlaska
50.0938443198.72016-01-03201613AnnetteANNAnnette, AKAlaska
60.05303624916.462016-01-03201613BethelBETBethel, AKAlaska
70.152232923.12016-01-03201613BettlesBTTBettles, AKAlaska
80.6343631209.12016-01-03201613Cold BayCDBCold Bay, AKAlaska
92.1538433399.762016-01-03201613CordovaCDVCordova, AKAlaska
precipitationavg_tempmax_tempmin_tempwind_directionwind_speeddate_fullyearmonthweek_ofcitycodelocationstate
167330.61445335227.682017-01-01201711HuntingtonHTSHuntington, WVWest Virginia
167340.352834212410.532017-01-01201711Green BayGRBGreen Bay, WIWisconsin
167350.04283520259.512017-01-01201711La CrosseLSELa Crosse, WIWisconsin
167360.11273419256.332017-01-01201711MadisonMSNMadison, WIWisconsin
167370.153138232510.982017-01-01201711MilwaukeeMKEMilwaukee, WIWisconsin
167380.082332152319.982017-01-01201711CasperCPRCasper, WYWyoming
167390.03242212615.162017-01-01201711CheyenneCYSCheyenne, WYWyoming
167400.017294261.652017-01-01201711LanderLNDLander, WYWyoming
167410.062331132418.162017-01-01201711RawlinsRWLRawlins, WYWyoming
167420.121348237.512017-01-01201711SheridanSHRSheridan, WYWyoming